Data Scientist – Agentic AI & Machine Learning

Addo AI is seeking a skilled Data Scientist with 2 to 3 years of experience in both traditional machine learning and Agentic AI. The successful candidate will be responsible for designing, building, and deploying machine learning models as well as large language model (LLM)-powered agentic systems that address real-world business challenges. This role demands strong Python programming skills, a solid foundation in statistics and machine learning, and hands-on experience in developing AI agents and orchestrated workflows.

Key Responsibilities:
- Design, develop, and deploy AI agents and multi-step agentic workflows utilizing LLMs, tool calling, and orchestration frameworks.
- Build Retrieval-Augmented Generation (RAG) pipelines, including document processing, embeddings, vector databases, and retrieval optimization.
- Develop, train, and evaluate traditional machine learning models for various tasks such as classification, regression, forecasting, clustering, and recommendation.
- Conduct data collection, cleaning, exploratory data analysis (EDA), and feature engineering on both structured and unstructured datasets.
- Integrate AI agents with external tools, APIs, databases, and enterprise systems to ensure seamless functionality.
- Apply prompt engineering, guardrails, and evaluation techniques to enhance the accuracy, reliability, and safety of LLM-based solutions.
- Determine the most appropriate approach for each problem, whether it involves classical ML models, LLM-based agents, or hybrid solutions.
- Write production-quality Python code and SQL queries; support deployment, monitoring, and optimization of models and agents.
- Collaborate closely with engineering, product, and client teams to translate business requirements into effective technical solutions.
- Stay updated with the latest advancements in Agentic AI, LLMs, and machine learning, and incorporate relevant innovations into projects.

Required Qualifications:
- Bachelor’s or Master’s degree in Computer Science, Data Science, AI, Statistics, or a related discipline.
- 2 to 3 years of professional experience in data science or machine learning, including practical work on Agentic AI or LLM-based projects.
- Strong proficiency in Python and libraries such as Pandas, NumPy, and Scikit-learn; experience with XGBoost/LightGBM and PyTorch or TensorFlow is advantageous.
- Hands-on experience with LLM and agent frameworks like LangChain, LangGraph, LlamaIndex, CrewAI, AutoGen, or similar platforms.
- Familiarity with LLM APIs such as OpenAI, Anthropic, Azure OpenAI, Gemini, and open-source models.
- Experience working with vector databases (e.g., Pinecone, FAISS, Chroma, Weaviate, pgvector) and building RAG systems.
- Solid understanding of machine learning algorithms, statistics, model evaluation, and experimentation methodologies.
- Proficient in SQL and experienced in managing large datasets.
- Knowledge of deploying models and services using APIs (FastAPI, Flask), containerization tools like Docker, and cloud platforms such as AWS, Azure, or GCP.
- Strong analytical, problem-solving, and communication skills, with the ability to convey complex technical concepts to non-technical stakeholders.

Preferred Qualifications and Benefits:
- Experience with LLM evaluation and observability tools such as LangSmith, Langfuse, or RAGAS.
- Exposure to fine-tuning LLMs or working with Hugging Face models.
- Familiarity with MLOps practices including MLflow, CI/CD pipelines, and model monitoring.
- Experience delivering AI solutions for enterprise clients, particularly in sectors like banking, finance, or retail.

This role offers the opportunity to work at the cutting edge of AI technology, contributing to impactful projects that leverage both classical machine learning and advanced agentic AI systems. Candidates passionate about innovation and problem-solving in a collaborative environment will find this position rewarding.